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ANN-Based Axial Strength Prediction of Short Columns with Double and Bar-Reinforced Concrete-Filled Steel Tubes Subjected to Concentric and Eccentric Loading

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Abstract

This study is aimed to predict axial compressive strength of circular concrete-filled double steel tube (CFDST) and reinforced concrete-filled steel tubular (RCFST) column subjected to concentric and eccentric loading using artificial neural network (ANN). In this context, initially an extensive literature review was carried out to compile a comprehensive database related to CFDST and RCFST column. Three separate databases were compiled related to (1) CFDST column under concentric loading, (2) CFDST column under eccentric loading, and (3) RCFST column under concentric loading. Neural network toolbox in MATLAB was used to develop and train ANN models. Key ANN features like network type, activation function, number of hidden layers, epochs and number of neurons in each layer were finalized after optimizing regression value. Additionally, sensitivity analysis was performed to understand the influence of various geometric and material parameters on the design strength. It was observed that geometric configurations like diameter of outer and inner steel tubes in case of CFDST and thickness, height, diameter in case of RCFST significantly influenced the axial strength as compared to material grade of concrete and steel tubes. The mean for the ratio of predictions and experimental observations were 0.962 (for CFDST) and 0.981 (for RCFST). Also, standard deviation for such ratio were 0.082 for both specimen type. Predicted results by ANN depicted that axial strength obtained using present formulation are coherent and even sometimes closer to experimental evidences compared to empirical type predictions from several design codes.

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Acknowledgements

This work was supported by research grant from the Department of Science and Technology—Science and Engineering Research Board (DST-SERB) India.

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No funds, grants or other support was received.

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Author notes

  1. Sukanta Chakraborty and Sanket Nayak have contributed equally to this work.

Authors and Affiliations

  1. Department of Civil Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad, Dhanbad, Jharkhand, 826004, India

    Sajal Sarkar, Sukanta Chakraborty & Sanket Nayak

  2. Department of Civil Engineering, Sikkim Manipal Institute of Technology, Majhitar, Sikkim, 737136, India

    Sajal Sarkar

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  1. Sajal Sarkar
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  2. Sukanta Chakraborty
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  3. Sanket Nayak
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Sajal Sarkar, Sukanta Chakraborty and Sanket Nayak. The first draft of the manuscript was written by Sajal Sarkar and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Sukanta Chakraborty.

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It is confirmed that this is an original paper which has neither previously been published nor simultaneously been submitted anywhere else. The authors have no financial or proprietary interests in any material discussed in this article. This research does not involve any Human Participants and/or Animals

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Appendix A: Database Compiled for Circular, CFDST and RCFST Columns

Appendix A: Database Compiled for Circular, CFDST and RCFST Columns

See Tables 9, 10 and 11.

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Sarkar, S., Chakraborty, S. & Nayak, S. ANN-Based Axial Strength Prediction of Short Columns with Double and Bar-Reinforced Concrete-Filled Steel Tubes Subjected to Concentric and Eccentric Loading. Arab J Sci Eng 49, 4947–4968 (2024). https://doi.org/10.1007/s13369-023-08285-8

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  • DOI: https://doi.org/10.1007/s13369-023-08285-8

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